Overview

Dataset statistics

Number of variables24
Number of observations744
Missing cells612
Missing cells (%)3.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory139.6 KiB
Average record size in memory192.2 B

Variable types

Categorical7
DateTime1
Numeric16

Alerts

name has constant value ""Constant
severerisk has constant value ""Constant
stations is highly imbalanced (82.9%)Imbalance
preciptype has 612 (82.3%) missing valuesMissing
datetime has unique valuesUnique
temp has 13 (1.7%) zerosZeros
precip has 617 (82.9%) zerosZeros
snow has 718 (96.5%) zerosZeros
snowdepth has 573 (77.0%) zerosZeros
cloudcover has 77 (10.3%) zerosZeros
solarradiation has 467 (62.8%) zerosZeros
solarenergy has 478 (64.2%) zerosZeros
uvindex has 546 (73.4%) zerosZeros

Reproduction

Analysis started2024-04-11 04:40:06.235740
Analysis finished2024-04-11 04:41:57.714778
Duration1 minute and 51.48 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

name
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
New York City,USA
744 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters12648
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York City,USA
2nd rowNew York City,USA
3rd rowNew York City,USA
4th rowNew York City,USA
5th rowNew York City,USA

Common Values

ValueCountFrequency (%)
New York City,USA 744
100.0%

Length

2024-04-11T00:41:58.061242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-11T00:41:58.518375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
new 744
33.3%
york 744
33.3%
city,usa 744
33.3%

Most occurring characters

ValueCountFrequency (%)
1488
 
11.8%
N 744
 
5.9%
e 744
 
5.9%
w 744
 
5.9%
Y 744
 
5.9%
o 744
 
5.9%
r 744
 
5.9%
k 744
 
5.9%
C 744
 
5.9%
i 744
 
5.9%
Other values (6) 4464
35.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5952
47.1%
Uppercase Letter 4464
35.3%
Space Separator 1488
 
11.8%
Other Punctuation 744
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 744
12.5%
w 744
12.5%
o 744
12.5%
r 744
12.5%
k 744
12.5%
i 744
12.5%
t 744
12.5%
y 744
12.5%
Uppercase Letter
ValueCountFrequency (%)
N 744
16.7%
Y 744
16.7%
C 744
16.7%
U 744
16.7%
S 744
16.7%
A 744
16.7%
Space Separator
ValueCountFrequency (%)
1488
100.0%
Other Punctuation
ValueCountFrequency (%)
, 744
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10416
82.4%
Common 2232
 
17.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 744
 
7.1%
e 744
 
7.1%
w 744
 
7.1%
Y 744
 
7.1%
o 744
 
7.1%
r 744
 
7.1%
k 744
 
7.1%
C 744
 
7.1%
i 744
 
7.1%
t 744
 
7.1%
Other values (4) 2976
28.6%
Common
ValueCountFrequency (%)
1488
66.7%
, 744
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12648
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1488
 
11.8%
N 744
 
5.9%
e 744
 
5.9%
w 744
 
5.9%
Y 744
 
5.9%
o 744
 
5.9%
r 744
 
5.9%
k 744
 
5.9%
C 744
 
5.9%
i 744
 
5.9%
Other values (6) 4464
35.3%

datetime
Date

UNIQUE 

Distinct744
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
Minimum2024-01-01 00:00:00
Maximum2024-01-31 23:00:00
2024-04-11T00:41:58.786275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:59.262069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

temp
Real number (ℝ)

ZEROS 

Distinct103
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0102151
Minimum-7.2
Maximum12.9
Zeros13
Zeros (%)1.7%
Negative172
Negative (%)23.1%
Memory size5.9 KiB
2024-04-11T00:41:59.811354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-7.2
5-th percentile-5
Q10.5
median3.3
Q36.2
95-th percentile9.4
Maximum12.9
Range20.1
Interquartile range (IQR)5.7

Descriptive statistics

Standard deviation4.2594148
Coefficient of variation (CV)1.4149869
Kurtosis-0.37751219
Mean3.0102151
Median Absolute Deviation (MAD)2.85
Skewness-0.22225103
Sum2239.6
Variance18.142614
MonotonicityNot monotonic
2024-04-11T00:42:00.375073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.9 36
 
4.8%
3.3 33
 
4.4%
2.2 32
 
4.3%
6.7 29
 
3.9%
2.9 29
 
3.9%
3.8 27
 
3.6%
2.8 26
 
3.5%
6.2 24
 
3.2%
5 21
 
2.8%
1.7 20
 
2.7%
Other values (93) 467
62.8%
ValueCountFrequency (%)
-7.2 2
 
0.3%
-6.7 3
 
0.4%
-6.6 1
 
0.1%
-6.2 5
 
0.7%
-6.1 1
 
0.1%
-5.7 9
1.2%
-5.2 1
 
0.1%
-5.1 7
0.9%
-5 17
2.3%
-4.9 1
 
0.1%
ValueCountFrequency (%)
12.9 2
 
0.3%
12.4 1
 
0.1%
12.3 4
0.5%
12.2 3
0.4%
11.9 1
 
0.1%
11.8 5
0.7%
11.7 2
 
0.3%
11.3 1
 
0.1%
11.2 1
 
0.1%
11 1
 
0.1%

feelslike
Real number (ℝ)

Distinct203
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10483871
Minimum-14
Maximum12.9
Zeros3
Zeros (%)0.4%
Negative336
Negative (%)45.2%
Memory size5.9 KiB
2024-04-11T00:42:01.010178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-14
5-th percentile-9.4
Q1-3.425
median0.7
Q33.8
95-th percentile8.155
Maximum12.9
Range26.9
Interquartile range (IQR)7.225

Descriptive statistics

Standard deviation5.3781401
Coefficient of variation (CV)51.299183
Kurtosis-0.24564546
Mean0.10483871
Median Absolute Deviation (MAD)3.5
Skewness-0.16998844
Sum78
Variance28.924391
MonotonicityNot monotonic
2024-04-11T00:42:01.525677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.9 16
 
2.2%
2.1 12
 
1.6%
-2.2 11
 
1.5%
2.8 11
 
1.5%
-4 10
 
1.3%
2 9
 
1.2%
0.8 9
 
1.2%
4 9
 
1.2%
4.7 9
 
1.2%
5.3 9
 
1.2%
Other values (193) 639
85.9%
ValueCountFrequency (%)
-14 1
0.1%
-12.9 1
0.1%
-12.7 1
0.1%
-12.6 1
0.1%
-12.4 2
0.3%
-12.3 1
0.1%
-12.2 1
0.1%
-12.1 1
0.1%
-12 1
0.1%
-11.9 1
0.1%
ValueCountFrequency (%)
12.9 2
 
0.3%
12.4 1
 
0.1%
12.3 4
0.5%
12.2 3
0.4%
11.9 1
 
0.1%
11.8 5
0.7%
11.7 2
 
0.3%
11.3 1
 
0.1%
11.2 1
 
0.1%
11 1
 
0.1%

dew
Real number (ℝ)

Distinct120
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.5938172
Minimum-14.1
Maximum11.8
Zeros4
Zeros (%)0.5%
Negative524
Negative (%)70.4%
Memory size5.9 KiB
2024-04-11T00:42:02.093326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-14.1
5-th percentile-12.3
Q1-6.8
median-2.3
Q31.1
95-th percentile7.885
Maximum11.8
Range25.9
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation5.9100106
Coefficient of variation (CV)-2.2784993
Kurtosis-0.46610873
Mean-2.5938172
Median Absolute Deviation (MAD)4
Skewness0.081111787
Sum-1929.8
Variance34.928226
MonotonicityNot monotonic
2024-04-11T00:42:02.734635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.4 29
 
3.9%
-0.7 28
 
3.8%
2.8 27
 
3.6%
-2.9 25
 
3.4%
-6.3 21
 
2.8%
-2.3 20
 
2.7%
-1.2 19
 
2.6%
1.6 18
 
2.4%
-2.2 18
 
2.4%
5 16
 
2.2%
Other values (110) 523
70.3%
ValueCountFrequency (%)
-14.1 2
 
0.3%
-14 1
 
0.1%
-13.6 7
0.9%
-13.5 8
1.1%
-13.4 1
 
0.1%
-13.2 1
 
0.1%
-13.1 1
 
0.1%
-13 2
 
0.3%
-12.5 6
0.8%
-12.4 7
0.9%
ValueCountFrequency (%)
11.8 1
 
0.1%
11.7 1
 
0.1%
11.2 5
0.7%
11.1 1
 
0.1%
10.7 9
1.2%
10.1 1
 
0.1%
9.4 3
 
0.4%
9 1
 
0.1%
8.9 4
0.5%
8.5 1
 
0.1%

humidity
Real number (ℝ)

Distinct623
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.31703
Minimum26.07
Maximum96.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-04-11T00:42:03.025990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum26.07
5-th percentile43.686
Q154.82
median66.69
Q384.83
95-th percentile92.8585
Maximum96.36
Range70.29
Interquartile range (IQR)30.01

Descriptive statistics

Standard deviation16.312612
Coefficient of variation (CV)0.23877812
Kurtosis-1.1448479
Mean68.31703
Median Absolute Deviation (MAD)14.09
Skewness0.075035344
Sum50827.87
Variance266.1013
MonotonicityNot monotonic
2024-04-11T00:42:03.300082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
92.87 7
 
0.9%
92.65 4
 
0.5%
88.93 4
 
0.5%
88.77 3
 
0.4%
84.95 3
 
0.4%
91.87 3
 
0.4%
93.3 3
 
0.4%
51.03 3
 
0.4%
66.36 3
 
0.4%
68.17 3
 
0.4%
Other values (613) 708
95.2%
ValueCountFrequency (%)
26.07 1
0.1%
29.02 1
0.1%
32.69 1
0.1%
34.04 1
0.1%
36.61 1
0.1%
39.37 1
0.1%
39.45 1
0.1%
39.78 1
0.1%
39.94 1
0.1%
39.97 1
0.1%
ValueCountFrequency (%)
96.36 1
 
0.1%
96.33 1
 
0.1%
96.24 1
 
0.1%
96.1 1
 
0.1%
95.25 1
 
0.1%
95.24 2
0.3%
94.25 1
 
0.1%
93.33 1
 
0.1%
93.3 3
0.4%
93.28 2
0.3%

precip
Real number (ℝ)

ZEROS 

Distinct64
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.010427419
Minimum0
Maximum0.524
Zeros617
Zeros (%)82.9%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-04-11T00:42:03.880224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.047
Maximum0.524
Range0.524
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.04451703
Coefficient of variation (CV)4.2692279
Kurtosis64.950022
Mean0.010427419
Median Absolute Deviation (MAD)0
Skewness7.4192751
Sum7.758
Variance0.0019817659
MonotonicityNot monotonic
2024-04-11T00:42:04.240964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 617
82.9%
0.019 15
 
2.0%
0.014 15
 
2.0%
0.028 7
 
0.9%
0.005 6
 
0.8%
0.031 5
 
0.7%
0.016 4
 
0.5%
0.022 4
 
0.5%
0.041 3
 
0.4%
0.02 3
 
0.4%
Other values (54) 65
 
8.7%
ValueCountFrequency (%)
0 617
82.9%
0.003 2
 
0.3%
0.005 6
 
0.8%
0.006 2
 
0.3%
0.009 2
 
0.3%
0.011 2
 
0.3%
0.012 1
 
0.1%
0.014 15
 
2.0%
0.015 2
 
0.3%
0.016 4
 
0.5%
ValueCountFrequency (%)
0.524 1
0.1%
0.453 1
0.1%
0.429 1
0.1%
0.4 1
0.1%
0.373 1
0.1%
0.316 1
0.1%
0.243 1
0.1%
0.217 1
0.1%
0.173 1
0.1%
0.17 1
0.1%

precipprob
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
0
617 
100
127 

Length

Max length3
Median length1
Mean length1.3413978
Min length1

Characters and Unicode

Total characters998
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 617
82.9%
100 127
 
17.1%

Length

2024-04-11T00:42:04.618380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-11T00:42:04.946348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 617
82.9%
100 127
 
17.1%

Most occurring characters

ValueCountFrequency (%)
0 871
87.3%
1 127
 
12.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 998
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 871
87.3%
1 127
 
12.7%

Most occurring scripts

ValueCountFrequency (%)
Common 998
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 871
87.3%
1 127
 
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 998
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 871
87.3%
1 127
 
12.7%

preciptype
Categorical

MISSING 

Distinct3
Distinct (%)2.3%
Missing612
Missing (%)82.3%
Memory size5.9 KiB
rain
104 
rain,snow
21 
snow
 
7

Length

Max length9
Median length4
Mean length4.7954545
Min length4

Characters and Unicode

Total characters633
Distinct characters8
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrain
2nd rowrain
3rd rowrain
4th rowrain
5th rowrain,snow

Common Values

ValueCountFrequency (%)
rain 104
 
14.0%
rain,snow 21
 
2.8%
snow 7
 
0.9%
(Missing) 612
82.3%

Length

2024-04-11T00:42:05.216330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-11T00:42:05.748412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
rain 104
78.8%
rain,snow 21
 
15.9%
snow 7
 
5.3%

Most occurring characters

ValueCountFrequency (%)
n 153
24.2%
r 125
19.7%
a 125
19.7%
i 125
19.7%
s 28
 
4.4%
o 28
 
4.4%
w 28
 
4.4%
, 21
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 612
96.7%
Other Punctuation 21
 
3.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 153
25.0%
r 125
20.4%
a 125
20.4%
i 125
20.4%
s 28
 
4.6%
o 28
 
4.6%
w 28
 
4.6%
Other Punctuation
ValueCountFrequency (%)
, 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 612
96.7%
Common 21
 
3.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 153
25.0%
r 125
20.4%
a 125
20.4%
i 125
20.4%
s 28
 
4.6%
o 28
 
4.6%
w 28
 
4.6%
Common
ValueCountFrequency (%)
, 21
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 633
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 153
24.2%
r 125
19.7%
a 125
19.7%
i 125
19.7%
s 28
 
4.4%
o 28
 
4.4%
w 28
 
4.4%
, 21
 
3.3%

snow
Real number (ℝ)

ZEROS 

Distinct16
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02983871
Minimum0
Maximum4.6
Zeros718
Zeros (%)96.5%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-04-11T00:42:06.125249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4.6
Range4.6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.25501057
Coefficient of variation (CV)8.5463002
Kurtosis189.5361
Mean0.02983871
Median Absolute Deviation (MAD)0
Skewness12.756371
Sum22.2
Variance0.065030391
MonotonicityNot monotonic
2024-04-11T00:42:06.624368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 718
96.5%
0.1 5
 
0.7%
0.2 4
 
0.5%
0.3 3
 
0.4%
0.6 2
 
0.3%
0.5 2
 
0.3%
1 1
 
0.1%
1.4 1
 
0.1%
4.6 1
 
0.1%
0.7 1
 
0.1%
Other values (6) 6
 
0.8%
ValueCountFrequency (%)
0 718
96.5%
0.1 5
 
0.7%
0.2 4
 
0.5%
0.3 3
 
0.4%
0.4 1
 
0.1%
0.5 2
 
0.3%
0.6 2
 
0.3%
0.7 1
 
0.1%
0.8 1
 
0.1%
1 1
 
0.1%
ValueCountFrequency (%)
4.6 1
0.1%
3.3 1
0.1%
2.3 1
0.1%
2 1
0.1%
1.4 1
0.1%
1.3 1
0.1%
1 1
0.1%
0.8 1
0.1%
0.7 1
0.1%
0.6 2
0.3%

snowdepth
Real number (ℝ)

ZEROS 

Distinct57
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7733871
Minimum0
Maximum6.7
Zeros573
Zeros (%)77.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-04-11T00:42:07.079546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4.8
Maximum6.7
Range6.7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.6533961
Coefficient of variation (CV)2.1378636
Kurtosis2.837073
Mean0.7733871
Median Absolute Deviation (MAD)0
Skewness2.0251293
Sum575.4
Variance2.7337188
MonotonicityNot monotonic
2024-04-11T00:42:07.549604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 573
77.0%
3 26
 
3.5%
3.4 16
 
2.2%
4.8 6
 
0.8%
4.6 6
 
0.8%
3.5 6
 
0.8%
6.2 5
 
0.7%
5 5
 
0.7%
4.3 5
 
0.7%
0.1 5
 
0.7%
Other values (47) 91
 
12.2%
ValueCountFrequency (%)
0 573
77.0%
0.1 5
 
0.7%
0.2 5
 
0.7%
0.3 2
 
0.3%
0.4 3
 
0.4%
0.5 2
 
0.3%
0.6 4
 
0.5%
0.8 2
 
0.3%
0.9 1
 
0.1%
1 1
 
0.1%
ValueCountFrequency (%)
6.7 1
 
0.1%
6.4 4
0.5%
6.3 3
0.4%
6.2 5
0.7%
6.1 4
0.5%
6 2
 
0.3%
5.9 2
 
0.3%
5.8 2
 
0.3%
5.7 1
 
0.1%
5.4 1
 
0.1%

windgust
Real number (ℝ)

Distinct192
Distinct (%)25.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.977823
Minimum5.4
Maximum85.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-04-11T00:42:07.992573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5.4
5-th percentile9.4
Q116.6
median27.7
Q338.9
95-th percentile56.095
Maximum85.9
Range80.5
Interquartile range (IQR)22.3

Descriptive statistics

Standard deviation15.559772
Coefficient of variation (CV)0.53695448
Kurtosis-0.094870377
Mean28.977823
Median Absolute Deviation (MAD)11.1
Skewness0.63728375
Sum21559.5
Variance242.10649
MonotonicityNot monotonic
2024-04-11T00:42:08.459557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.6 51
 
6.9%
18.4 51
 
6.9%
14.8 45
 
6.0%
11.2 41
 
5.5%
27.7 39
 
5.2%
9.4 33
 
4.4%
13 31
 
4.2%
7.6 23
 
3.1%
25.9 21
 
2.8%
37.1 21
 
2.8%
Other values (182) 388
52.2%
ValueCountFrequency (%)
5.4 9
 
1.2%
7.6 23
3.1%
9.4 33
4.4%
11.2 41
5.5%
13 31
4.2%
14.8 45
6.0%
16.6 51
6.9%
18.4 51
6.9%
20.5 18
 
2.4%
22.3 16
 
2.2%
ValueCountFrequency (%)
85.9 1
0.1%
84.4 1
0.1%
79.7 1
0.1%
77.8 2
0.3%
76 1
0.1%
72 1
0.1%
68.4 2
0.3%
67.9 1
0.1%
66.6 1
0.1%
66.1 1
0.1%

windspeed
Real number (ℝ)

Distinct212
Distinct (%)28.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.798522
Minimum0
Maximum39.5
Zeros3
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-04-11T00:42:08.976602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5
Q17.6
median11.15
Q316.6
95-th percentile29
Maximum39.5
Range39.5
Interquartile range (IQR)9

Descriptive statistics

Standard deviation8.0872154
Coefficient of variation (CV)0.63188669
Kurtosis0.23559037
Mean12.798522
Median Absolute Deviation (MAD)4.85
Skewness0.747834
Sum9522.1
Variance65.403053
MonotonicityNot monotonic
2024-04-11T00:42:09.495747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.9 18
 
2.4%
9.2 15
 
2.0%
14.6 14
 
1.9%
7.7 14
 
1.9%
9.4 13
 
1.7%
12.8 12
 
1.6%
5.5 12
 
1.6%
14.5 11
 
1.5%
11.3 11
 
1.5%
0.2 11
 
1.5%
Other values (202) 613
82.4%
ValueCountFrequency (%)
0 3
 
0.4%
0.1 7
0.9%
0.2 11
1.5%
0.3 8
1.1%
0.4 7
0.9%
0.5 10
1.3%
0.7 4
 
0.5%
0.8 5
0.7%
0.9 3
 
0.4%
1 6
0.8%
ValueCountFrequency (%)
39.5 1
0.1%
37.1 1
0.1%
36.4 1
0.1%
36.3 1
0.1%
36.1 1
0.1%
35.9 1
0.1%
35.4 1
0.1%
34.6 1
0.1%
34.4 1
0.1%
34.3 1
0.1%

winddir
Real number (ℝ)

Distinct182
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202.22581
Minimum0
Maximum360
Zeros6
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-04-11T00:42:09.918751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.15
Q161
median252
Q3273
95-th percentile352
Maximum360
Range360
Interquartile range (IQR)212

Descriptive statistics

Standard deviation114.9467
Coefficient of variation (CV)0.56840766
Kurtosis-1.2373776
Mean202.22581
Median Absolute Deviation (MAD)53
Skewness-0.54816695
Sum150456
Variance13212.743
MonotonicityNot monotonic
2024-04-11T00:42:10.516249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
270 31
 
4.2%
260 27
 
3.6%
261 23
 
3.1%
250 20
 
2.7%
269 17
 
2.3%
252 17
 
2.3%
50 16
 
2.2%
360 15
 
2.0%
259 15
 
2.0%
41 15
 
2.0%
Other values (172) 548
73.7%
ValueCountFrequency (%)
0 6
0.8%
1 5
0.7%
2 8
1.1%
3 7
0.9%
4 4
0.5%
5 3
 
0.4%
6 3
 
0.4%
7 2
 
0.3%
8 1
 
0.1%
9 1
 
0.1%
ValueCountFrequency (%)
360 15
2.0%
359 7
0.9%
358 3
 
0.4%
357 2
 
0.3%
355 1
 
0.1%
354 2
 
0.3%
353 4
 
0.5%
352 8
1.1%
351 2
 
0.3%
350 7
0.9%

sealevelpressure
Real number (ℝ)

Distinct304
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1017.537
Minimum982.7
Maximum1035.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-04-11T00:42:11.077145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum982.7
5-th percentile999.845
Q11012.725
median1018.85
Q31023.2
95-th percentile1033.2
Maximum1035.8
Range53.1
Interquartile range (IQR)10.475

Descriptive statistics

Standard deviation9.8795542
Coefficient of variation (CV)0.0097092828
Kurtosis1.101537
Mean1017.537
Median Absolute Deviation (MAD)4.75
Skewness-0.78434036
Sum757047.5
Variance97.60559
MonotonicityNot monotonic
2024-04-11T00:42:11.641651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1016.4 12
 
1.6%
1023.1 10
 
1.3%
1021.1 9
 
1.2%
1016.8 9
 
1.2%
1016.6 8
 
1.1%
1018.9 8
 
1.1%
1032.4 8
 
1.1%
1018.5 8
 
1.1%
1020.8 8
 
1.1%
1023 7
 
0.9%
Other values (294) 657
88.3%
ValueCountFrequency (%)
982.7 1
0.1%
983 1
0.1%
983.5 1
0.1%
984.6 1
0.1%
986.2 2
0.3%
987.2 2
0.3%
988.9 1
0.1%
989 1
0.1%
989.1 1
0.1%
989.3 2
0.3%
ValueCountFrequency (%)
1035.8 1
 
0.1%
1035.6 2
 
0.3%
1035.3 1
 
0.1%
1035.2 1
 
0.1%
1035 1
 
0.1%
1034.9 1
 
0.1%
1034.8 2
 
0.3%
1034.7 2
 
0.3%
1034.6 3
0.4%
1034.5 5
0.7%

cloudcover
Real number (ℝ)

ZEROS 

Distinct69
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.927285
Minimum0
Maximum100
Zeros77
Zeros (%)10.3%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-04-11T00:42:12.222783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.35
median99.6
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)98.65

Descriptive statistics

Standard deviation45.757606
Coefficient of variation (CV)0.71577584
Kurtosis-1.5876119
Mean63.927285
Median Absolute Deviation (MAD)0.4
Skewness-0.58839055
Sum47561.9
Variance2093.7585
MonotonicityNot monotonic
2024-04-11T00:42:12.724236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 333
44.8%
0.4 78
 
10.5%
0 77
 
10.3%
99.6 59
 
7.9%
1.5 37
 
5.0%
0.8 28
 
3.8%
98.6 15
 
2.0%
88.1 8
 
1.1%
1.7 8
 
1.1%
89.1 7
 
0.9%
Other values (59) 94
 
12.6%
ValueCountFrequency (%)
0 77
10.3%
0.3 1
 
0.1%
0.4 78
10.5%
0.7 1
 
0.1%
0.8 28
 
3.8%
0.9 1
 
0.1%
1.5 37
5.0%
1.7 8
 
1.1%
2.1 1
 
0.1%
2.7 2
 
0.3%
ValueCountFrequency (%)
100 333
44.8%
99.9 2
 
0.3%
99.8 1
 
0.1%
99.6 59
 
7.9%
99 4
 
0.5%
98.6 15
 
2.0%
96.8 1
 
0.1%
95.4 1
 
0.1%
95.2 1
 
0.1%
95 3
 
0.4%

visibility
Real number (ℝ)

Distinct62
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.48078
Minimum0.6
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-04-11T00:42:13.806556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile5
Q115.9
median16
Q316
95-th percentile16
Maximum16
Range15.4
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation3.3969694
Coefficient of variation (CV)0.23458471
Kurtosis4.07781
Mean14.48078
Median Absolute Deviation (MAD)0
Skewness-2.2815144
Sum10773.7
Variance11.539401
MonotonicityNot monotonic
2024-04-11T00:42:14.264167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 538
72.3%
15.9 22
 
3.0%
14.1 15
 
2.0%
15.8 13
 
1.7%
9.8 9
 
1.2%
10.7 8
 
1.1%
10.8 7
 
0.9%
9.7 7
 
0.9%
6 6
 
0.8%
4.9 6
 
0.8%
Other values (52) 113
 
15.2%
ValueCountFrequency (%)
0.6 1
 
0.1%
1.2 1
 
0.1%
1.4 1
 
0.1%
2 3
0.4%
2.9 2
0.3%
3 4
0.5%
3.1 1
 
0.1%
3.3 2
0.3%
3.8 1
 
0.1%
3.9 4
0.5%
ValueCountFrequency (%)
16 538
72.3%
15.9 22
 
3.0%
15.8 13
 
1.7%
15.7 2
 
0.3%
15.6 4
 
0.5%
15.5 1
 
0.1%
15.4 5
 
0.7%
15.3 5
 
0.7%
14.1 15
 
2.0%
13.8 3
 
0.4%

solarradiation
Real number (ℝ)

ZEROS 

Distinct155
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.387097
Minimum0
Maximum626
Zeros467
Zeros (%)62.8%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-04-11T00:42:14.953467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q358.5
95-th percentile391.25
Maximum626
Range626
Interquartile range (IQR)58.5

Descriptive statistics

Standard deviation119.61824
Coefficient of variation (CV)2.08441
Kurtosis6.3367507
Mean57.387097
Median Absolute Deviation (MAD)0
Skewness2.6197402
Sum42696
Variance14308.523
MonotonicityNot monotonic
2024-04-11T00:42:15.476431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 467
62.8%
21 7
 
0.9%
11 5
 
0.7%
88 5
 
0.7%
28 5
 
0.7%
60 5
 
0.7%
23 5
 
0.7%
86 4
 
0.5%
26 4
 
0.5%
84 4
 
0.5%
Other values (145) 233
31.3%
ValueCountFrequency (%)
0 467
62.8%
3 1
 
0.1%
5 1
 
0.1%
8 1
 
0.1%
9 1
 
0.1%
11 5
 
0.7%
12 2
 
0.3%
14 2
 
0.3%
15 1
 
0.1%
16 2
 
0.3%
ValueCountFrequency (%)
626 1
0.1%
617 1
0.1%
548 1
0.1%
547 1
0.1%
540 1
0.1%
526 1
0.1%
515 1
0.1%
512 1
0.1%
508 1
0.1%
504 1
0.1%

solarenergy
Real number (ℝ)

ZEROS 

Distinct23
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20672043
Minimum0
Maximum2.3
Zeros478
Zeros (%)64.2%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-04-11T00:42:15.891434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.2
95-th percentile1.4
Maximum2.3
Range2.3
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.43153985
Coefficient of variation (CV)2.087553
Kurtosis6.3742537
Mean0.20672043
Median Absolute Deviation (MAD)0
Skewness2.6241118
Sum153.8
Variance0.18622665
MonotonicityNot monotonic
2024-04-11T00:42:16.366715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 478
64.2%
0.1 56
 
7.5%
0.3 46
 
6.2%
0.2 40
 
5.4%
0.5 19
 
2.6%
0.4 16
 
2.2%
0.6 11
 
1.5%
1.5 9
 
1.2%
0.7 8
 
1.1%
1.8 8
 
1.1%
Other values (13) 53
 
7.1%
ValueCountFrequency (%)
0 478
64.2%
0.1 56
 
7.5%
0.2 40
 
5.4%
0.3 46
 
6.2%
0.4 16
 
2.2%
0.5 19
 
2.6%
0.6 11
 
1.5%
0.7 8
 
1.1%
0.8 6
 
0.8%
0.9 8
 
1.1%
ValueCountFrequency (%)
2.3 1
 
0.1%
2.2 1
 
0.1%
2 2
 
0.3%
1.9 3
 
0.4%
1.8 8
1.1%
1.7 7
0.9%
1.6 6
0.8%
1.5 9
1.2%
1.4 4
0.5%
1.3 6
0.8%

uvindex
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56586022
Minimum0
Maximum6
Zeros546
Zeros (%)73.4%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-04-11T00:42:16.674906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2182842
Coefficient of variation (CV)2.1529774
Kurtosis5.8164644
Mean0.56586022
Median Absolute Deviation (MAD)0
Skewness2.5376547
Sum421
Variance1.4842165
MonotonicityNot monotonic
2024-04-11T00:42:16.902076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 546
73.4%
1 109
 
14.7%
2 29
 
3.9%
5 23
 
3.1%
4 22
 
3.0%
3 13
 
1.7%
6 2
 
0.3%
ValueCountFrequency (%)
0 546
73.4%
1 109
 
14.7%
2 29
 
3.9%
3 13
 
1.7%
4 22
 
3.0%
5 23
 
3.1%
6 2
 
0.3%
ValueCountFrequency (%)
6 2
 
0.3%
5 23
 
3.1%
4 22
 
3.0%
3 13
 
1.7%
2 29
 
3.9%
1 109
 
14.7%
0 546
73.4%

severerisk
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
10
744 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1488
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row10
3rd row10
4th row10
5th row10

Common Values

ValueCountFrequency (%)
10 744
100.0%

Length

2024-04-11T00:42:17.120488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-11T00:42:17.372872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
10 744
100.0%

Most occurring characters

ValueCountFrequency (%)
1 744
50.0%
0 744
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1488
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 744
50.0%
0 744
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1488
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 744
50.0%
0 744
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1488
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 744
50.0%
0 744
50.0%

conditions
Categorical

Distinct8
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
Overcast
300 
Clear
240 
Rain, Overcast
101 
Partially cloudy
77 
Snow, Rain, Overcast
 
17
Other values (3)
 
9

Length

Max length22
Median length20
Mean length9.0188172
Min length4

Characters and Unicode

Total characters6710
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.3%

Sample

1st rowOvercast
2nd rowOvercast
3rd rowOvercast
4th rowOvercast
5th rowOvercast

Common Values

ValueCountFrequency (%)
Overcast 300
40.3%
Clear 240
32.3%
Rain, Overcast 101
 
13.6%
Partially cloudy 77
 
10.3%
Snow, Rain, Overcast 17
 
2.3%
Snow, Overcast 7
 
0.9%
Rain 1
 
0.1%
Rain, Partially cloudy 1
 
0.1%

Length

2024-04-11T00:42:17.621446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-11T00:42:17.958332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
overcast 425
44.0%
clear 240
24.9%
rain 120
 
12.4%
partially 78
 
8.1%
cloudy 78
 
8.1%
snow 24
 
2.5%

Most occurring characters

ValueCountFrequency (%)
a 941
14.0%
r 743
11.1%
e 665
9.9%
c 503
 
7.5%
t 503
 
7.5%
l 474
 
7.1%
O 425
 
6.3%
v 425
 
6.3%
s 425
 
6.3%
C 240
 
3.6%
Other values (12) 1366
20.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5459
81.4%
Uppercase Letter 887
 
13.2%
Space Separator 221
 
3.3%
Other Punctuation 143
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 941
17.2%
r 743
13.6%
e 665
12.2%
c 503
9.2%
t 503
9.2%
l 474
8.7%
v 425
7.8%
s 425
7.8%
i 198
 
3.6%
y 156
 
2.9%
Other values (5) 426
7.8%
Uppercase Letter
ValueCountFrequency (%)
O 425
47.9%
C 240
27.1%
R 120
 
13.5%
P 78
 
8.8%
S 24
 
2.7%
Space Separator
ValueCountFrequency (%)
221
100.0%
Other Punctuation
ValueCountFrequency (%)
, 143
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6346
94.6%
Common 364
 
5.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 941
14.8%
r 743
11.7%
e 665
10.5%
c 503
7.9%
t 503
7.9%
l 474
7.5%
O 425
6.7%
v 425
6.7%
s 425
6.7%
C 240
 
3.8%
Other values (10) 1002
15.8%
Common
ValueCountFrequency (%)
221
60.7%
, 143
39.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6710
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 941
14.0%
r 743
11.1%
e 665
9.9%
c 503
 
7.5%
t 503
 
7.5%
l 474
 
7.1%
O 425
 
6.3%
v 425
 
6.3%
s 425
 
6.3%
C 240
 
3.6%
Other values (12) 1366
20.4%

icon
Categorical

Distinct7
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
cloudy
300 
clear-night
158 
rain
97 
clear-day
82 
partly-cloudy-night
40 
Other values (2)
67 

Length

Max length19
Median length17
Mean length8.297043
Min length4

Characters and Unicode

Total characters6173
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcloudy
2nd rowcloudy
3rd rowcloudy
4th rowcloudy
5th rowcloudy

Common Values

ValueCountFrequency (%)
cloudy 300
40.3%
clear-night 158
21.2%
rain 97
 
13.0%
clear-day 82
 
11.0%
partly-cloudy-night 40
 
5.4%
partly-cloudy-day 37
 
5.0%
snow 30
 
4.0%

Length

2024-04-11T00:42:18.241000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-11T00:42:18.529819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
cloudy 300
40.3%
clear-night 158
21.2%
rain 97
 
13.0%
clear-day 82
 
11.0%
partly-cloudy-night 40
 
5.4%
partly-cloudy-day 37
 
5.0%
snow 30
 
4.0%

Most occurring characters

ValueCountFrequency (%)
l 694
11.2%
c 617
10.0%
y 573
9.3%
a 533
 
8.6%
d 496
 
8.0%
r 414
 
6.7%
o 407
 
6.6%
- 394
 
6.4%
u 377
 
6.1%
n 325
 
5.3%
Other values (8) 1343
21.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5779
93.6%
Dash Punctuation 394
 
6.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 694
12.0%
c 617
10.7%
y 573
9.9%
a 533
9.2%
d 496
8.6%
r 414
 
7.2%
o 407
 
7.0%
u 377
 
6.5%
n 325
 
5.6%
i 295
 
5.1%
Other values (7) 1048
18.1%
Dash Punctuation
ValueCountFrequency (%)
- 394
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5779
93.6%
Common 394
 
6.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 694
12.0%
c 617
10.7%
y 573
9.9%
a 533
9.2%
d 496
8.6%
r 414
 
7.2%
o 407
 
7.0%
u 377
 
6.5%
n 325
 
5.6%
i 295
 
5.1%
Other values (7) 1048
18.1%
Common
ValueCountFrequency (%)
- 394
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6173
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 694
11.2%
c 617
10.0%
y 573
9.3%
a 533
 
8.6%
d 496
 
8.0%
r 414
 
6.7%
o 407
 
6.6%
- 394
 
6.4%
u 377
 
6.1%
n 325
 
5.3%
Other values (8) 1343
21.8%

stations
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
684 
72505394728,72055399999,KLGA,KJRB,F1417,KNYC
 
51
72505394728,72055399999,KJRB,F1417,KNYC
 
3
72505394728,72055399999,KLGA,KJRB,F1417,72503014732
 
2
72055399999,KLGA,KJRB,F1417,KNYC,72503014732
 
2
Other values (2)
 
2

Length

Max length56
Median length56
Mean length55.024194
Min length39

Characters and Unicode

Total characters40938
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.3%

Sample

1st row72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
2nd row72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
3rd row72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
4th row72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
5th row72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732

Common Values

ValueCountFrequency (%)
72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732 684
91.9%
72505394728,72055399999,KLGA,KJRB,F1417,KNYC 51
 
6.9%
72505394728,72055399999,KJRB,F1417,KNYC 3
 
0.4%
72505394728,72055399999,KLGA,KJRB,F1417,72503014732 2
 
0.3%
72055399999,KLGA,KJRB,F1417,KNYC,72503014732 2
 
0.3%
72055399999,KLGA,KJRB,F1417,72503014732 1
 
0.1%
72505394728,KLGA,KJRB,F1417,KNYC,72503014732 1
 
0.1%

Length

2024-04-11T00:42:18.829229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-11T00:42:19.126011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
72505394728,72055399999,klga,kjrb,f1417,knyc,72503014732 684
91.9%
72505394728,72055399999,klga,kjrb,f1417,knyc 51
 
6.9%
72505394728,72055399999,kjrb,f1417,knyc 3
 
0.4%
72505394728,72055399999,klga,kjrb,f1417,72503014732 2
 
0.3%
72055399999,klga,kjrb,f1417,knyc,72503014732 2
 
0.3%
72055399999,klga,kjrb,f1417,72503014732 1
 
0.1%
72505394728,klga,kjrb,f1417,knyc,72503014732 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
9 4456
10.9%
, 4400
10.7%
7 4349
10.6%
5 3658
8.9%
2 3605
8.8%
0 2864
 
7.0%
3 2864
 
7.0%
K 2226
 
5.4%
1 2178
 
5.3%
4 2175
 
5.3%
Other values (11) 8163
19.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26890
65.7%
Uppercase Letter 9648
 
23.6%
Other Punctuation 4400
 
10.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
K 2226
23.1%
F 744
 
7.7%
J 744
 
7.7%
R 744
 
7.7%
B 744
 
7.7%
Y 741
 
7.7%
N 741
 
7.7%
L 741
 
7.7%
A 741
 
7.7%
G 741
 
7.7%
Decimal Number
ValueCountFrequency (%)
9 4456
16.6%
7 4349
16.2%
5 3658
13.6%
2 3605
13.4%
0 2864
10.7%
3 2864
10.7%
1 2178
8.1%
4 2175
8.1%
8 741
 
2.8%
Other Punctuation
ValueCountFrequency (%)
, 4400
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 31290
76.4%
Latin 9648
 
23.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
K 2226
23.1%
F 744
 
7.7%
J 744
 
7.7%
R 744
 
7.7%
B 744
 
7.7%
Y 741
 
7.7%
N 741
 
7.7%
L 741
 
7.7%
A 741
 
7.7%
G 741
 
7.7%
Common
ValueCountFrequency (%)
9 4456
14.2%
, 4400
14.1%
7 4349
13.9%
5 3658
11.7%
2 3605
11.5%
0 2864
9.2%
3 2864
9.2%
1 2178
7.0%
4 2175
7.0%
8 741
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40938
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 4456
10.9%
, 4400
10.7%
7 4349
10.6%
5 3658
8.9%
2 3605
8.8%
0 2864
 
7.0%
3 2864
 
7.0%
K 2226
 
5.4%
1 2178
 
5.3%
4 2175
 
5.3%
Other values (11) 8163
19.9%

Interactions

2024-04-11T00:41:47.753525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:08.325006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:15.014795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:18.764335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:22.263290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:28.597578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:34.411979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:41.964361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:49.512009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:56.599278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:04.594769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:12.169846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:19.805222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:26.894473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:33.714608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:40.746336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:48.252652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:08.698092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:15.286057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:18.993850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:22.504580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:29.154537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:34.646528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:42.668741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:50.083038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:57.142720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:05.014303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:12.621853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:20.179824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:27.345448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:34.294310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:41.248084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:48.662112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:08.977901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:15.514377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:19.180382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:22.715178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:29.479142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:34.845731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:43.118988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:50.335668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:58.030846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:05.513907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:13.060864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:20.728104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:27.694873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:34.711895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:41.612326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:49.103451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:09.265182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:15.730480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:19.379607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:23.193846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:29.918602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:35.047578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:43.651685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:50.646163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:58.486750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:05.876926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:13.531956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:21.145252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:28.015056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:35.048830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:42.110659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:49.514255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:09.568475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:15.969460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:19.658608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:23.661770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:30.145651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:35.505949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:44.329195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:51.077772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:58.851400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:06.289369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:13.862250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:21.645161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:28.576895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:35.400028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:42.596071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:49.877345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:09.797447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:16.195666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:19.888043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:24.295912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:30.513719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:35.963088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:44.730047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:51.638053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:59.305884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:06.770575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:14.363427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:22.012718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:28.997916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:35.877898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:42.945840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:50.340848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:10.031425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:16.396774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:20.081099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:24.514184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:30.781179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:36.455015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:45.125449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:52.228767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:59.718664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:07.255236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:14.930613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:22.345452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:29.443070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:36.371447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:43.277527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:50.711720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:10.282227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:16.651284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:20.296094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:24.782022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:31.245858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:37.030919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:45.546938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:52.580130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:00.063727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:07.659954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:15.313467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:22.779943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:29.839343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:36.891660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:43.595603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:51.129000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:10.548833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:16.901681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:20.537349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:25.573275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:31.530594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:37.380700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:46.159896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:53.082957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:00.683963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:08.070740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:15.856291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:23.278149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:30.312618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:37.262495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:44.062505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:51.713127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:11.165666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:17.147892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:20.797448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:25.918226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:32.088366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:37.930503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:46.701399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:53.651644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:01.172071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:08.668952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:16.086795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:23.703127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:30.795901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:37.610764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:44.462999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:52.163822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:11.655838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:17.385287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:21.028902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:26.147822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:32.497741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:38.380772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:47.114546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:54.127921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:01.638788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:09.080131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:16.528898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:24.185376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:31.277638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:38.068241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:45.026809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:52.528675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:11.902025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:17.599532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:21.234132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:26.447632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:32.930715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:38.753766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:47.464149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:54.658578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:02.165307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:09.446900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:17.560868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:24.628117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:31.761809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:38.561312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:45.428943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:53.590793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:12.130662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:17.830779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:21.424906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:26.886126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:33.230125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:39.226168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:47.947696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:55.147285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:02.611438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:10.046847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:17.962137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:25.046390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:32.164195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:38.979541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:45.897738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:54.112835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:12.404942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:18.097649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:21.647790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:27.297745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:33.730383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:39.697262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:48.413976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:55.413257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:03.111516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:10.570779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:18.527915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:25.511507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:32.628823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:39.479073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:46.394020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:54.563948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:14.556060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:18.314287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:21.846463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:27.664408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:33.947044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:40.164802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:48.763331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:55.696898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:03.611259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:11.114075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:18.970942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:26.028166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:33.007631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:39.995429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:46.830167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:55.046332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:14.784477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:18.528977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:22.047041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:28.263025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:34.164679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:40.616847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:49.096739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:40:56.322401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:04.096468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:11.643465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:19.378767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:26.444108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:33.346470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:40.411927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:41:47.344479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Missing values

2024-04-11T00:41:55.688016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-11T00:41:56.959688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

namedatetimetempfeelslikedewhumidityprecipprecipprobpreciptypesnowsnowdepthwindgustwindspeedwinddirsealevelpressurecloudcovervisibilitysolarradiationsolarenergyuvindexsevereriskconditionsiconstations
0New York City,USA2024-01-01T00:00:005.72.7-0.863.080.0000NaN0.00.014.814.52501016.699.616.000.0010Overcastcloudy72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
1New York City,USA2024-01-01T01:00:005.72.6-0.863.260.0000NaN0.00.018.414.52601016.4100.016.000.0010Overcastcloudy72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
2New York City,USA2024-01-01T02:00:005.73.2-0.863.390.0000NaN0.00.016.611.12701016.2100.016.000.0010Overcastcloudy72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
3New York City,USA2024-01-01T03:00:005.02.5-0.766.340.0000NaN0.00.014.810.72631016.2100.016.000.0010Overcastcloudy72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
4New York City,USA2024-01-01T04:00:005.03.3-1.264.250.0000NaN0.00.013.07.52501015.8100.016.000.0010Overcastcloudy72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
5New York City,USA2024-01-01T05:00:005.03.3-0.766.740.0000NaN0.00.07.67.22531015.899.016.000.0010Overcastcloudy72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
6New York City,USA2024-01-01T06:00:005.03.3-0.766.640.0000NaN0.00.07.67.62621016.3100.016.000.0010Overcastcloudy72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
7New York City,USA2024-01-01T07:00:004.33.10.676.830.014100rain0.00.07.65.52951016.7100.015.800.0010Rain, Overcastrain72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
8New York City,USA2024-01-01T08:00:004.94.91.679.220.019100rain0.00.07.60.33451017.1100.015.9110.0010Rain, Overcastrain72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
9New York City,USA2024-01-01T09:00:005.02.81.176.310.006100rain0.00.07.69.23301017.4100.016.0700.3110Rain, Overcastrain72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
namedatetimetempfeelslikedewhumidityprecipprecipprobpreciptypesnowsnowdepthwindgustwindspeedwinddirsealevelpressurecloudcovervisibilitysolarradiationsolarenergyuvindexsevereriskconditionsiconstations
734New York City,USA2024-01-31T14:00:003.93.9-0.772.040.00NaN0.00.07.60.161020.1100.016.0840.3110Overcastcloudy72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
735New York City,USA2024-01-31T15:00:003.92.0-0.871.920.00NaN0.00.07.67.22761020.1100.016.0630.2110Overcastcloudy72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
736New York City,USA2024-01-31T16:00:003.92.7-0.772.260.00NaN0.00.09.45.41931020.0100.016.0420.2010Overcastcloudy72505394728,72055399999,KLGA,KJRB,F1417,KNYC
737New York City,USA2024-01-31T17:00:003.92.7-0.772.210.00NaN0.00.067.95.32001019.7100.016.000.0010Overcastcloudy72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
738New York City,USA2024-01-31T18:00:003.41.4-0.774.640.00NaN0.00.09.47.32611019.8100.016.000.0010Overcastcloudy72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
739New York City,USA2024-01-31T19:00:003.42.1-0.774.510.00NaN0.00.09.45.52591019.6100.016.000.0010Overcastcloudy72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
740New York City,USA2024-01-31T20:00:003.42.1-0.774.510.00NaN0.00.05.45.32521019.3100.016.000.0010Overcastcloudy72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
741New York City,USA2024-01-31T21:00:003.42.1-0.774.630.00NaN0.00.05.45.32521019.1100.016.000.0010Overcastcloudy72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
742New York City,USA2024-01-31T22:00:002.91.6-0.180.770.00NaN0.00.05.45.42601019.199.616.000.0010Overcastcloudy72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732
743New York City,USA2024-01-31T23:00:002.90.80.080.910.00NaN0.00.07.67.62331019.2100.016.000.0010Overcastcloudy72505394728,72055399999,KLGA,KJRB,F1417,KNYC,72503014732